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import multiprocessing as mp |
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import torch |
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import os |
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from functools import partial |
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import gradio as gr |
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import traceback |
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from tts.infer_cli import MegaTTS3DiTInfer, convert_to_wav, cut_wav |
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import spaces |
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os.system('huggingface-cli download ByteDance/MegaTTS3 --local-dir ./checkpoints --repo-type model') |
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CUDA_AVAILABLE = torch.cuda.is_available() |
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infer_pipe = MegaTTS3DiTInfer(device='cuda' if CUDA_AVAILABLE else 'cpu') |
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@spaces.GPU(duration=120) |
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def forward_gpu(file_content, latent_file, inp_text, time_step, p_w, t_w): |
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resource_context = infer_pipe.preprocess(file_content, latent_file) |
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wav_bytes = infer_pipe.forward(resource_context, inp_text, time_step=time_step, p_w=p_w, t_w=t_w) |
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return wav_bytes |
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def model_worker(input_queue, output_queue, device_id): |
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while True: |
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task = input_queue.get() |
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inp_audio_path, inp_npy_path, inp_text, infer_timestep, p_w, t_w = task |
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try: |
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convert_to_wav(inp_audio_path) |
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wav_path = os.path.splitext(inp_audio_path)[0] + '.wav' |
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cut_wav(wav_path, max_len=24) |
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with open(wav_path, 'rb') as file: |
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file_content = file.read() |
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wav_bytes = forward_gpu(file_content, inp_npy_path, inp_text, time_step=infer_timestep, p_w=p_w, t_w=t_w) |
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output_queue.put(wav_bytes) |
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except Exception as e: |
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traceback.print_exc() |
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print(task, str(e)) |
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output_queue.put(None) |
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def main(inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w, processes, input_queue, output_queue): |
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print("Push task to the inp queue |", inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w) |
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input_queue.put((inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w)) |
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res = output_queue.get() |
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if res is not None: |
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return res |
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else: |
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print("") |
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return None |
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if __name__ == '__main__': |
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mp.set_start_method('spawn', force=True) |
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mp_manager = mp.Manager() |
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num_workers = 1 |
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devices = [0] |
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input_queue = mp_manager.Queue() |
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output_queue = mp_manager.Queue() |
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processes = [] |
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print("Start open workers") |
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for i in range(num_workers): |
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p = mp.Process(target=model_worker, args=(input_queue, output_queue, i % len(devices) if devices is not None else None)) |
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p.start() |
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processes.append(p) |
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api_interface = gr.Interface(fn= |
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partial(main, processes=processes, input_queue=input_queue, |
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output_queue=output_queue), |
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inputs=[gr.Audio(type="filepath", label="Upload .wav"), gr.File(type="filepath", label="Upload .npy"), "text", |
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gr.Number(label="infer timestep", value=32), |
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gr.Number(label="Intelligibility Weight", value=1.4), |
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gr.Number(label="Similarity Weight", value=3.0)], outputs=[gr.Audio(label="Synthesized Audio")], |
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title="MegaTTS3", |
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description="Upload a speech clip as a reference for timbre, " + |
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"upload the pre-extracted latent file, "+ |
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"input the target text, and receive the cloned voice. "+ |
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"Tip: a generation process should be within 120s (check if your input text are too long). Please use the system gently, as excessive load or languages other than English or Chinese may cause crashes and disrupt access for other users.", concurrency_limit=1) |
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api_interface.launch(server_name='0.0.0.0', server_port=7860, debug=True) |
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for p in processes: |
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p.join() |
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